OpenRaiser/Intern-Atlas huggingface dataset:
Soft decoding without soft demapping with ORBGRAND
Venue: arXiv cs.IT 2022
Year: 2022

For spectral efficiency, higher order modulation symbols confer information on more than one bit. As soft detection forward error correction decoders assume the availability of information at binary granularity, however, soft demappers are required to compute per-bit reliabilities from complex-valued signals. Here we show that the recently introduced universal soft detection decoder ORBGRAND can be adapted to work with symbol-level soft information, obviating the need for energy expensive soft demapping. We establish that doing so reduces complexity while retaining the error correction performance achieved with the optimal demapper.
Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis
Venue: arXiv cs.NI 2022
Year: 2022

This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm's performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo
Venue: arXiv cs.CV 2022
Year: 2022

Multi-view stereo is an important research task in computer vision while still keeping challenging. In recent years, deep learning-based methods have shown superior performance on this task. Cost volume pyramid network-based methods which progressively refine depth map in coarse-to-fine manner, have yielded promising results while consuming less memory. However, these methods fail to take fully consideration of the characteristics of the cost volumes in each stage, leading to adopt similar range search strategies for each cost volume stage. In this work, we present a novel cost volume pyramid based network with different searching strategies for multi-view stereo. By choosing different depth range sampling strategies and applying adaptive unimodal filtering, we are able to obtain more accurate depth estimation in low resolution stages and iteratively upsample depth map to arbitrary resolution. We conducted extensive experiments on both DTU and BlendedMVS datasets, and results show that our method outperforms most state-of-the-art methods.
A Dataset Generation Framework for profiling Disassembly attacks using Side-Channel Leakages and Deep Neural Networks
Venue: arXiv cs.CR 2022
Year: 2022

Various studies among side-channel attacks have tried to extract information through leakages from electronic devices to reach the instruction flow of some appliances. However, previous methods highly depend on the resolution of traced data. Obtaining low-noise traces is not always feasible in real attack scenarios. This study proposes two deep models to extract low and high-level features from side-channel traces and classify them to related instructions. We aim to evaluate the accuracy of a side-channel attack on low-resolution data with a more robust feature extractor thanks to neural networks. As inves-tigated, instruction flow in real programs is predictable and follows specific distributions. This leads to proposing a LSTM model to estimate these distributions, which could expedite the reverse engineering process and also raise the accuracy. The proposed model for leakage classification reaches 54.58% accuracy on average and outperforms other existing methods on our datasets. Also, LSTM model reaches 94.39% accuracy for instruction prediction on standard implementation of cryptographic algorithms.
Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster
Venue: arXiv cs.DC 2022
Year: 2022

These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and identify anomalous behaviours that occur during its operation. In recent years, the utilisation of \ac{rnn} and \ac{dnn} to detect anomalies that might occur during runtime has been a trending approach. However, it is unclear how to explain the decisions made by these networks and how these networks should be interpreted to understand the runtime behaviour that they model. On the contrary, state machine models provide an easier manner to interpret and understand the behaviour that they model. In this work, we propose an approach that learns state machine models to model the runtime behaviour of a cloud environment that runs multiple microservice applications. To the best of our knowledge, this is the first work that tries to apply state machine models to microservice architectures. The state machine model is used to detect the different types of attacks that we launch on the cloud environment. From our experiment results, our approach can detect the attacks very well, achieving a balanced accuracy of 99.2% and an F1 score of 0.982.
New technological trajectories and research directions in Cloud Computing Technology, 2004-2021
Venue: arXiv cs.DC 2022
Year: 2022

The goal of this study is to explore emerging trends in cloud computing technology that can support an economic and social change. We apply the methods of entity linking, which links word strings to entities from a knowledge base, to extract main keywords in cloud computing from accumulated publications from 2004 to 2021. Results suggest that in cloud computing research, Internet of things has an accelerated technological and scientific growth compared to the other topics. Other critical research fields that support the evolution of cloud computing are mathematical optimization and virtual machine. These findings reveal a technological competition between Cloud systems infrastructure, hardware development side, and computing and software development to play the main role in technological evolution of Cloud Computing. Moreover, this study shows that technological development of virtual machines and computing device can be of critical importance to foster an economic and technological change in many sectors. However, the implementation of cloud computing has to be supported by skill development, organizational change, and adopter engagement, to foster the management and the diffusion of cloud technologies and the exploitation of cloud-based infrastructures for competitive advantage of firms in fast-changing markets. This study can provide main information to extend the knowledge having theoretical implications to explain characteristics of the evolution of science and technology in this field of research and practical implications of innovation management for the appropriate allocation of resources towards new technological trajectories in cloud computing having a potential of growth and beneficial impact in society.
AutoDiCE: Fully Automated Distributed CNN Inference at the Edge
Venue: arXiv cs.DC 2022
Year: 2022

Deep Learning approaches based on Convolutional Neural Networks (CNNs) are extensively utilized and very successful in a wide range of application areas, including image classification and speech recognition. For the execution of trained CNNs, i.e. model inference, we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute and memory intensive CNNs on edge devices is challenging because these devices typically have limited power budgets and compute/memory resources. One approach to address this challenge is to leverage all available resources across multiple edge devices to deploy and execute a large CNN by properly partitioning the CNN and running each CNN partition on a separate edge device. Although such distribution, deployment, and execution of large CNNs on multiple edge devices is a desirable and beneficial approach, there currently does not exist a design and programming framework that takes a trained CNN model, together with a CNN partitioning specification, and fully automates the CNN model splitting and deployment on multiple edge devices to facilitate distributed CNN inference at the Edge. Therefore, in this paper, we propose a novel framework, called AutoDiCE, for automated splitting of a CNN model into a set of sub-models and automated code generation for distributed and collaborative execution of these sub-models on multiple, possibly heterogeneous, edge devices, while supporting the exploitation of parallelism among and within the edge devices. Our experimental results show that AutoDiCE can deliver distributed CNN inference with reduced energy consumption and memory usage per edge device, and improved overall system throughput at the same time.
Hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a system for hardware-in-the-loop (HiL) simulation of unmanned aerial vehicle (UAV) control algorithms implemented on a heterogeneous SoC FPGA computing platforms. The AirSim simulator running on a PC and an Arty Z7 development board with a Zynq SoC chip from AMD Xilinx were used. Communication was carried out via a serial USB link. An application for autonomous landing on a specially marked landing strip was selected as a case study. A landing site detection algorithm was implemented on the Zynq SoC platform. This allowed processing a 1280 x 720 @ 60 fps video stream in real time. Performed tests showed that the system works correctly and there are no delays that could negatively affect the stability of the control. The proposed concept is characterised by relative simplicity and low implementation cost. At the same time, it can be applied to test various types of high-level perception and control algorithms for UAV implemented on embedded platforms. We provide the code developed on GitHub, which includes both Python scripts running on the PC and C code running on Arty Z7.
FAT-PIM: Low-Cost Error Detection for Processing-In-Memory
Venue: arXiv cs.AR 2022
Year: 2022

Processing In Memory (PIM) accelerators are promising architecture that can provide massive parallelization and high efficiency in various applications. Such architectures can instantaneously provide ultra-fast operation over extensive data, allowing real-time performance in data-intensive workloads. For instance, Resistive Memory (ReRAM) based PIM architectures are widely known for their inherent dot-product computation capability. While the performance of such architecture is essential, reliability and accuracy are also important, especially in mission-critical real-time systems. Unfortunately, the PIM architectures have a fundamental limitation in guaranteeing error-free operation. As a result, current methods must pay high implementation costs or performance penalties to achieve reliable execution in the PIM accelerator. In this paper, we make a fundamental observation of this reliability limitation of ReRAM based PIM architecture. Accordingly, we propose a novel solution--Falut Tolerant PIM or FAT-PIM, that can improve reliability for such systems significantly at a low cost. Our evaluation shows that we can improve the error tolerance significantly with only 4.9% performance cost and 3.9% storage overhead.
A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform
Venue: arXiv cs.RO 2022
Year: 2022

Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform.
Continual Few-Shot Learning with Adversarial Class Storage
Venue: arXiv cs.LG 2022
Year: 2022

Humans have a remarkable ability to quickly and effectively learn new concepts in a continuous manner without forgetting old knowledge. Though deep learning has made tremendous successes on various computer vision tasks, it faces challenges for achieving such human-level intelligence. In this paper, we define a new problem called continual few-shot learning, in which tasks arrive sequentially and each task is associated with a few training samples. We propose Continual Meta-Learner (CML) to solve this problem. CML integrates metric-based classification and a memory-based mechanism along with adversarial learning into a meta-learning framework, which leads to the desirable properties: 1) it can quickly and effectively learn to handle a new task; 2) it overcomes catastrophic forgetting; 3) it is model-agnostic. We conduct extensive experiments on two image datasets, MiniImageNet and CIFAR100. Experimental results show that CML delivers state-of-the-art performance in terms of classification accuracy on few-shot learning tasks without catastrophic forgetting.
Estimación de áreas de cultivo mediante Deep Learning y programación convencional
Venue: arXiv cs.CV 2022
Year: 2022

Artificial Intelligence has enabled the implementation of more accurate and efficient solutions to problems in various areas. In the agricultural sector, one of the main needs is to know at all times the extent of land occupied or not by crops in order to improve production and profitability. The traditional methods of calculation demand the collection of data manually and in person in the field, causing high labor costs, execution times, and inaccuracy in the results. The present work proposes a new method based on Deep Learning techniques complemented with conventional programming for the determination of the area of populated and unpopulated crop areas. We have considered as a case study one of the most recognized companies in the planting and harvesting of sugar cane in Ecuador. The strategy combines a Generative Adversarial Neural Network (GAN) that is trained on a dataset of aerial photographs of natural and urban landscapes to improve image resolution; a Convolutional Neural Network (CNN) trained on a dataset of aerial photographs of sugar cane plots to distinguish populated or unpopulated crop areas; and a standard image processing module for the calculation of areas in a percentage manner. The experiments performed demonstrate a significant improvement in the quality of the aerial photographs as well as a remarkable differentiation between populated and unpopulated crop areas, consequently, a more accurate result of cultivated and uncultivated areas. The proposed method can be extended to the detection of possible pests, areas of weed vegetation, dynamic crop development, and both qualitative and quantitative quality control.
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
Venue: arXiv cs.CV 2022
Year: 2022

We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it includes high precision GPS-INS ground truth location data, high precision accelerometer readings, laser altimeter readings, and RGB downward facing camera imagery. In addition, we provide reference imagery over the flight paths, which makes this dataset suitable for VPR benchmarking and other tasks common in Localization, such as image registration and visual odometry. To the author's knowledge, this is the largest real-world aerial-vehicle dataset of this kind. Our dataset is available at https://github.com/MetaSLAM/ALTO.
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Venue: arXiv cs.LG 2022
Year: 2022

While single-agent policy optimization in a fixed environment has attracted a lot of research attention recently in the reinforcement learning community, much less is known theoretically when there are multiple agents playing in a potentially competitive environment. We take steps forward by proposing and analyzing new fictitious play policy optimization algorithms for zero-sum Markov games with structured but unknown transitions. We consider two classes of transition structures: factored independent transition and single-controller transition. For both scenarios, we prove tight $\widetilde{\mathcal{O}}(\sqrt{K})$ regret bounds after $K$ episodes in a two-agent competitive game scenario. The regret of each agent is measured against a potentially adversarial opponent who can choose a single best policy in hindsight after observing the full policy sequence. Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization in a non-stationary environment. When both players adopt the proposed algorithms, their overall optimality gap is $\widetilde{\mathcal{O}}(\sqrt{K})$.
Benchmark time series data sets for PyTorch -- the torchtime package
Venue: arXiv cs.LG 2022
Year: 2022

The development of models for Electronic Health Record data is an area of active research featuring a small number of public benchmark data sets. Researchers typically write custom data processing code but this hinders reproducibility and can introduce errors. The Python package torchtime provides reproducible implementations of commonly used PhysioNet and UEA & UCR time series classification repository data sets for PyTorch. Features are provided for working with irregularly sampled and partially observed time series of unequal length. It aims to simplify access to PhysioNet data and enable fair comparisons of models in this exciting area of research.
Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs
Venue: arXiv cs.LG 2022
Year: 2022

With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In marine transportation, this trade imbalance leads to Empty Container Repositioning (ECR) problems. Once the freight has been delivered from an exporting country to an importing one, the laden will turn into empty containers that need to be repositioned to satisfy new goods requests in exporting countries. In such problems, the performance that any cooperative repositioning policy can achieve strictly depends on the routes that vessels will follow (i.e., fleet deployment). Historically, Operation Research (OR) approaches were proposed to jointly optimize the repositioning policy along with the fleet of vessels. However, the stochasticity of future supply and demand of containers, together with black-box and non-linear constraints that are present within the environment, make these approaches unsuitable for these scenarios. In this paper, we introduce a novel framework, Configurable Semi-POMDPs, to model this type of problems. Furthermore, we provide a two-stage learning algorithm, "Configure & Conquer" (CC), that first configures the environment by finding an approximation of the optimal fleet deployment strategy, and then "conquers" it by learning an ECR policy in this tuned environmental setting. We validate our approach in large and real-world instances of the problem. Our experiments highlight that CC avoids the pitfalls of OR methods and that it is successful at optimizing both the ECR policy and the fleet of vessels, leading to superior performance in world trade environments.
The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

In general, large datasets enable deep learning models to perform with good accuracy and generalizability. However, massive high-fidelity simulation datasets (from molecular chemistry, astrophysics, computational fluid dynamics (CFD), etc. can be challenging to curate due to dimensionality and storage constraints. Lossy compression algorithms can help mitigate limitations from storage, as long as the overall data fidelity is preserved. To illustrate this point, we demonstrate that deep learning models, trained and tested on data from a petascale CFD simulation, are robust to errors introduced during lossy compression in a semantic segmentation problem. Our results demonstrate that lossy compression algorithms offer a realistic pathway for exposing high-fidelity scientific data to open-source data repositories for building community datasets. In this paper, we outline, construct, and evaluate the requirements for establishing a big data framework, demonstrated at https://blastnet.github.io/, for scientific machine learning.
Enabling a Cooperative Driver Messenger System for Lane Change Assistance Application
Venue: arXiv cs.NI 2022
Year: 2022

Sensor data and Vehicle-to-Everything (V2X) communication can greatly assist Connected and Autonomous Vehicles (CAVs) in situational awareness and provide a safer driving experience. While sensor data recorded from devices such as radar and camera can assist in local awareness in the close vicinity of the Host Vehicle (HV), the information obtained is useful solely for the HV itself. On the other hand, V2X communication can allow CAVs to communicate with each other and transceive basic and/or advanced safety information, allowing each CAV to create a sophisticated local object map for situational awareness. This paper introduces a point-to-point Driver Messenger System (DMS) that regularly maintains a local object map of the HV and uses it to convey HV's Over-the-Air (OTA) Driver Intent Messages (DIMs) to nearby identified Target Vehicle(s) (TV(s)) based on a list of pre-defined common traffic applications. The focus of this paper is on the lane change application where DMS can use the local object map to automatically identify closest TV in adjacent lane in the direction of HV's intended lane change and inform the TV via a DIM. Within DMS, the paper proposes a TV recognition algorithm for lane change application that utilizes the HV's Path History (PH) to accurately determine the closest TV that could potentially benefit from receiving a DIM from HV. Finally, DMS is also shown to act as an advanced warning system by providing extra time and space headway measurements between the HV and TVs upon a number of simulated lane change scenarios.
RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments
Venue: arXiv cs.CV 2022
Year: 2022

Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated. Relocalization in large-scale indoor environments enables attractive applications such as augmented reality and robot navigation. However, appearance changes fast in such environments when the camera moves, which is challenging for the relocalization system. To address this problem, we propose a virtual view synthesis-based approach, RenderNet, to enrich the database and refine poses regarding this particular scenario. Instead of rendering real images which requires high-quality 3D models, we opt to directly render the needed global and local features of virtual viewpoints and apply them in the subsequent image retrieval and feature matching operations respectively. The proposed method can largely improve the performance in large-scale indoor environments, e.g., achieving an improvement of 7.1\% and 12.2\% on the Inloc dataset.